How to visualize data

Visualizing the data we ’ re working with is indispensible both to check that data pipelines are set up correctly and to check the predictions of a trained model . For visualization, the Makie . jl plotting package is used which requires you to install a plotting backend . Learning tasks define how the data is visualized, allowing you to use the following functions for visualization:

showsample ( showsamples ): Visualize an unprocessed sample (usually a pair of inputs and targets) or a vector of samples .
showencodedsample ( showbatch ): Visualize processed model input and output x, y or a batch of xs and ys .
showprediction ( showpredictions ): Compare a model output with the ground truth .

To add support for these to a learning task, you have to implement the plotting interface for a block: showblock! .

Let ’ s look at an example using the Cat/Dog classifier from the saving and loading tutorial .


			
			import CairoMakie; CairoMakie.activate!(type="png")
using FastAI

task, model = loadtaskmodel("catsdogs.jld2")
dir = joinpath(datasetpath("dogscats"), "train")
data = loadfolderdata(dir, filterfn=isimagefile, loadfn=(loadfile, parentname))


			
			(mapobs(loadfile, ["/home/lorenz/.julia/datadeps/fastai-dogscats/dogscats/train/cats/cat.0.jpg", "…]), mapobs(parentname, ["/home/lorenz/.julia/datadeps/fastai-dogscats/dogscats/train/cats/cat.0.jpg", "…]))

First we load a vector of unprocessed samples, a batch of training data and the corresponding model outputs:


			
			idxs = rand(1:nobs(data), 9)
samples = [getobs(data, i) for i in idxs]
xs, ys = makebatch(task, data, idxs)
ŷs = gpu(model)(gpu(xs)) |> cpu


			
			2×9 Matrix{Float32}:
  2.81113  -2.68682    4.27251  -4.39263  …   2.25726  -2.52003  -4.23501
 -1.4409    0.872713  -4.20728   2.1302      -1.23509   1.5162    3.21313

Then we can visualize the data with the functions listed above:


			
			showsamples(task, samples)


			
			showbatch(task, xs, ys)


			
			showpredictions(task, xs, ŷs, ys)